Codeplay: Autotelic Learning through Collaborative Self-Play in Programming Environments

Published: 20 Oct 2023, Last Modified: 30 Nov 2023IMOL@NeurIPS2023EveryoneRevisionsBibTeX
Keywords: Deep reinforcement learning, language models, program synthesis, autotelic learning, intrinsic motivation, self-play, problem generation
Abstract: Autotelic learning is the training setup where agents learn by setting their own goals and trying to achieve them. However, creatively generating freeform goals is challenging for autotelic agents. We present Codeplay, an algorithm casting autotelic learning as a game between a Setter agent and a Solver agent, where the Setter generates programming puzzles of appropriate difficulty and novelty for the solver and the Solver learns to achieve them. Early experiments with the Setter demonstrates one can effectively control the tradeoff between difficulty of a puzzle and its novelty by tuning the reward of the Setter, a code language model finetuned with deep reinforcement learning.
Submission Number: 32